Technological Change and Catching-Up in the Indian Banking ...

Vol.:(0123456789)

Computational Economics (2020) 56:217–237https://doi.org/10.1007/s10614-020-09993-1

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Technological Change and Catching‑Up in the Indian Banking Sector: A Time‑Dependent Nonparametric Frontier Approach

Sushanta Mallick1 · Aarti Rughoo2 · Nickolaos G. Tzeremes3 · Wei Xu4

Accepted: 4 May 2020 / Published online: 18 May 2020 © The Author(s) 2020

AbstractThis paper investigates whether there has been any improvement in efficiency con-vergence of banks in India during the post-reform period considering bank own-ership structures, using a balanced panel for 73 banks over the time period 1996–2014. Utilizing nonparametric frontier estimators, we compute time-dependent bank efficiency scores, which allow us to examine the dynamics of technological frontier and catch-up levels of Indian banks, and to explore the convergence patterns in the estimated efficiency levels. Our results signify that the state-owned banks, which dominate the banking activity in India, establish themselves as the best perform-ers, ahead of the private, foreign and cooperative banks during post-2005. Even during the recent global financial crisis period, we find that bank efficiency levels increased, except for foreign banks which have had the greatest adverse impact. The convergence results show that heterogeneity is present in bank efficiency conver-gence, which points to the presence of club formation suggesting that Indian banks’ efficiency convergence is partly driven by the ownership structure.

Keywords Bank efficiency · Nonparametric frontiers · Conditional efficiency · Convergence · India

JEL Classification C14 · G21 · G28

1 Introduction

The first significant systemic changes of the Indian banking system can be traced back to the late 1960s when banks were nationalised. It has been argued that these changes were introduced rather chaotically having a weak impact on the reform of Indian banks (Fujii et al. 2014). Subsequently, it was felt that the positive and

* Sushanta Mallick [email protected]

Extended author information available on the last page of the article

http://orcid.org/0000-0003-3851-3680

http://crossmark.crossref.org/dialog/?doi=10.1007/s10614-020-09993-1&domain=pdf

218 S. Mallick et al.

1 3

systematic changes were only introduced in the 1990s in the form of the Narasim-ham Committee reports of 1991 and 1998, which outlined the main trajectory of further improvements. The main aim of the reforms was the promotion of compe-tition and market orientation promoting banks’ efficiency improvements (Kumar 2013). The related literature investigates bank efficiency in India during the last two decades (Kumbhakar and Sarkar 2003; Bhaumik and Dimova 2004; Das and Ghosh 2006; Bhattacharyya and Pal 2013, among others). However, there are limited stud-ies investigating bank performance in India in the more recent years (Fujii et al. 2014; Tzeremes 2015). Additionally, the empirical investigation of convergence patterns among Indian banks’ efficiency levels is largely an unexplored area in the related literature (Kumar and Gulati 2010; Kumar 2013).

In contrast to the related literature on the deregulation and efficiency in Indian banking, this paper makes some important contributions. First, we analyse bank efficiency using a time-dependent non-parametric frontier approach (Bădin et al. 2012; Mastromarco and Simar 2015). This novel approach does not presume that the restrictive “separability” condition among the inputs, the outputs and the time holds (Simar and Wilson 2007, 2011; Daraio et al. 2018). As a result, we assume that time influences banks’ inefficiency levels along with the shape and the level of the bound-ary of the attainable set. These measures enable us to incorporate dynamic effects on the estimated efficiency measures, grasping all the performance changes that are based on different time periods (Mallick et al. 2016; Mastromarco and Simar 2018; Tzeremes 2015, 2019). The applied efficiency estimators are most suited in our case since they enable us to identify the indirect effect of the reforms made over different time periods.

The second part of the paper then tests for convergence in bank efficiency scores using the group and club convergence methods of Phillips and Sul (2007, 2009). This is a robust approach being most suitable for nonparametric efficiency estima-tors, since it does not impose strong assumptions on the trend nor on the stochastic stationarity of the examined efficiency estimates (Chen et al. 2018). Third, we con-duct a comprehensive study by factoring all types of bank ownership structures in our analysis. We thus analyse banks’ efficiency levels on the basis of their ownership structures and test whether bank ownership structure influences the convergence process. Fourth, we are able to draw meaningful conclusions by considering a long dataset (1996–2014) that covers the entire reform period in India while spanning over the global financial crisis period.

The paper is structured in the following manner. Section 2 provides a summary of the related literature, whereas Sect. 3 discusses the methodological framework. In Sect. 4, we describe the data and present the empirical findings. Finally, Sect. 5 concludes the paper.

2 A Brief Snapshot of the Literature

Following the financial reforms during post-1991, considerable research exam-ining efficiency of Indian banks has emerged. The literature follows two main trajectories. First, several studies examine bank efficiency and productivity within

219

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Technological Change and Catching-Up in the Indian Banking…

the context of various bank ownership structures (Sarkar et al. 1998; Bhau-mik and Dimova 2004). Second, a large strand of studies examines the effect of restructuring process on banks’ stability, efficiency and productivity levels (Kumbhakar and Sarkar 2003; Tabak and Tecles 2010; Das and Kumbhakar 2012; Ahamed and Mallick 2017a, b). Most of these studies looking at bank efficiency following the reforms point to low and declining efficiency. For instance, Das and Ghosh (2006) have found low levels of technical efficiency coupled with a declining trend in efficiency. Similarly, the empirical evidence from Battacharyya and Pal (2013) indicates low and declining efficiency for most years, though an improvement was noted towards 2009. In addition, some of the empirical studies revealed varying efficiency levels on the basis of ownership structure. Bhaumik and Dimova (2004) provide evidence of higher performance for domestic private and foreign banks, with the state-owned banks catching-up from mid-1990s.

In their study, Sahoo and Tone (2009) reported that post 2002 all three types of banks experienced an upward trend in efficiency. They attributed the changes to the liberalisation process. Furthermore, they identified weak output and resource allocation performances for nationalised banks. On the other hand, Tabak and Tecles (2010) examined Indian banks’ performance levels over the period 2000–2006 and their results suggest the improvement of state-owned banks’ efficiency levels in relation to domestic private and foreign banks. According to them, the driving force of this performance change was attributed to the liberali-sation policy which in turn enhanced market competition.

In a comparable study, Sanyal and Shankar (2011) over the period 1992–2004 provide evidence that productivity gaps among banks with different ownership have widened after 1998. Fujii et al. (2014) showed that foreign banks perform better than the state-owned and domestic private banks. These results contradict the findings from earlier studies (Sanyal and Shankar 2011; Tabak and Tecles 2010) suggesting that state-owned banks are better performers. In a similar study, Tzeremes (2015) employed a conditional directional distance estimator to esti-mate bank efficiency over the period 2004–2012. The author confirmed previous findings that a bank’s ownership structure has a direct impact on its performance level. The study reported that the overall banking efficiency increased in India during the period 2004–2008, but decreased thereafter.

Stemming from the studies examining the effect of deregulation on the bank-ing industry, Sensarma (2008) found that banks’ efficiency and productivity lev-els have declined as a result of the deregulation policies. In contrast, Das and Kumbhakar (2012) in a different methodological framework provide evidence of a positive impact of deregulation on banks’ efficiency levels. Casu et al. (2013) suggest that banks’ ownership structure influences the way the banks’ responded to the deregulation reforms, suggesting that foreign banks dominate the banking activity in India.

To our knowledge, Kumar and Gulati (2010) and Kumar (2013) are the only stud-ies, so far, that analysed the convergence of Indian banks’ efficiency levels. Specifi-cally, Kumar and Gulati (2010) applied the concept of beta and sigma convergence to a sample of Indian public sector banks (PSBs) providing evidence of increased bank efficiency during the post reform period. Similarly, Kumar (2013) extended the


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study by Kumar and Gulati (2010) providing empirical evidence of a positive effect of deregulation policies on banks’ efficiency levels.

During the post reform period, both studies confirmed the presence of conver-gence among Indian banks’ efficiency levels, which requires re-examination, meas-uring efficiency in a time-dependent non-parametric set-up, while also considering the ownership structures.

3 Data and Methodology

3.1 Data Description

We model Indian banks’ production process utilizing the intermediation approach (Berger and Humphrey 1997). As a result, we argue that banks’ production pro-cess transforms deposits alongside production inputs in order to produce loans and other financial products (Sealey and Lindley 1977). Following the relevant literature (McKillop et al. 1996; Berger and Mester 1997; Drake and Hall 2003; Fukuyama and Matousek 2011; Degl’Innocenti et al. 2017), in our nonparametric efficiency measurement, we need to define banks’ production process. For that reason, we use as inputs: total fixed assets, total deposits and wages and salaries, whereas, as out-put, we use banks’ total loans (see Table 1 for descriptive statistics). Finally, our sample contains a balanced panel of 73 Indian banks with different ownership struc-tures over the period 1996–2014.

3.2 Time‑Dependent Efficiency Measures

By incorporating the developments of the relative literature (Daraio and Simar 2005, 2007; Jeong et al. 2010; Bădin et al. 2012),1 we can define bank’s production func-tion as a set of inputs � ∈ R

p

+ and outputs � ∈ Rq

+ . Then the bank’s production set of � with all technical feasible pairs of inputs and outputs (�, �) can be defined as:

Given a certain input–output level (�0, �0 ∈ �

) the input oriented efficiency score

can be presented as:

In Eq. (2), �0 indicates the input oriented efficiency score and identifies the propor-tionate reduction of bank’s inputs operating at

(�0, �0

) level in order to be technically

efficient. Furthermore, �0 takes values of ≤ 1 with a �0 = 1 suggesting that a bank is

(1)� ={(�, �) ∈ R

p

+ × Rq

+||� can produce �

}

(2)𝜗0 = 𝜗(𝜐0, 𝜅0

)= inf

{𝜗 > 0|

(𝜗𝜐0, 𝜅0

)∈ 𝛺

}.

1 For interesting applications using the probabilistic framework of efficiency measurement see the stud-ies by Gearhart III and Michieka (2018) and Cordero et al. (2018).

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input efficient. Then we utilize the probabilistic approach by Cazals et al. (2002) and define bank’s production set in (1) as:

Table 1 Descriptive statistics

Years Statistics Total fixed assets Total deposits Wages and salaries Total loans and securities

1996 Mean 81,378.595 96,301.381 1603.908 37,451.552Std 187,038.155 206,866.035 4193.022 87,240.205

1997 Mean 91,390.671 106,672.196 1685.832 39,315.477Std 203,710.546 221,257.887 4205.269 86,636.885

1998 Mean 108,056.634 127,152.582 1835.122 46,286.270Std 235,777.206 262,001.699 4517.937 101,528.838

1999 mean 129,111.801 150,510.605 2205.518 53,582.045Std 286,877.755 324,311.000 5617.822 120,561.449

2000 Mean 150,584.001 178,275.385 2453.285 64,423.126Std 332,084.273 378,110.488 5901.275 138,337.393

2001 Mean 174,848.830 210,142.358 3049.271 75,689.201Std 393,857.644 452,193.471 7618.124 157,928.213

2002 Mean 207,690.385 245,812.247 2864.804 91,157.738Std 445,491.963 504,579.781 6590.269 174,697.918

2003 Mean 230,096.737 278,147.078 3125.392 103,025.389Std 481,054.650 562,815.654 7207.918 195,657.430

2004 Mean 265,560.988 320,683.081 3544.674 117,802.533Std 528,755.218 626,236.174 8124.967 221,475.139

2005 Mean 310,459.455 392,311.785 3989.668 153,182.738Std 607,234.692 756,195.591 8936.809 286,164.267

2006 Mean 367,272.164 484,592.290 4407.156 202,950.568Std 690,041.935 900,211.636 10,108.868 379,505.550

2007 Mean 457,535.982 618,286.448 4767.977 265,319.030Std 827,387.362 1,118,209.520 10,070.681 493,880.691

2008 Mean 572,446.727 766,172.292 5290.595 330,984.708Std 1,027,509.681 1,354,028.759 10,120.402 601,368.230

2009 Mean 700,774.714 944,789.000 6505.081 405,645.640Std 1,298,752.825 1,740,176.613 12,615.245 753,260.447

2010 Mean 806,223.566 1,102,669.785 7489.460 472,475.411Std 1,436,563.454 1,967,965.962 15,877.201 866,849.209

2011 Mean 967,551.584 1,337,320.616 9569.534 584,339.033Std 1,697,321.888 2,359,165.301 20,279.363 1,053,892.337

2012 Mean 1,118,001.058 1,554,755.007 10,071.158 690,075.727Std 1,900,736.255 2,698,968.348 21,186.040 1,223,147.129

2013 Mean 1,284,507.821 1,791,613.514 11,344.126 797,568.267Std 2,203,224.114 3,136,658.019 23,135.844 1,437,350.575

2014 Mean 1,468,736.402 2,055,353.169 13,366.877 914,917.373Std 2,542,227.980 3,657,025.292 28,479.543 1,681,956.063


1 3

where Φ�K(�, �) = Prob(� ≤ �, K ≥ �) , implying free disposability of � . Then we can retrieve the following conditional probability Φ� |K = Prob(� ≤ �|K ≥ �) and we consider that:

where �K(�) = Prob(K ≥ �) . Then the banks’ efficiency score at (�0, �0

) level can

be redefined as:

where the empirical version of Φ� |K(�|�) can be obtained from:

As has been introduced by Mastromarco and Simar (2015),2 we can consider time T as a conditional variable and for every time period t (years in our case) we can define �t as the support of the following conditional probability Φt

� |K = Prob(� ≤ �|K ≥ �, T = t) and define �t as:

At a specific time period, let (�0, �0

) denote bank’s input and output levels. Then

the time dependent efficiency score can be obtained from:

As presented by Daouia and Simar (2007), we can retrieve the unconditional and the time dependent robust α-quantile frontiers for any a ∈ (0, 1) from:

We consider the time effect on banks’ input oriented measures (both for the full3 and robust frontiers) by constructing the following ratios from the condi-tional and unconditional measures:

(3)𝛺 =

{(𝜐, 𝜅) ∈ R

p+q

+

|||Φ𝛶K(𝜐, 𝜅) > 0},

(4)Φ�K(�, �) = Φ� |K(�|�)�K(�),

(5)𝜗(𝜐0, 𝜅0

)= inf

{𝜗 > 0|Φ𝛶 |K

(𝜗𝜐0

||𝜅0)> 0

},

(6)Φ̂𝛶 �K(𝜐�𝜅) =∑n

i=1I�𝛶i ≤ 𝜐, Ki ≥ 𝜅

�

∑n

i=1I�Ki ≥ 𝜅

� .

(7)𝛺t =

{(𝜐, 𝜅) ∈ R

p+q

+

|||Φt𝛶K

(𝜐, 𝜅) > 0}.

(8)𝜗t(𝜐0, 𝜅0

)= inf

{𝜗 > 0|

(𝜗𝜐0, 𝜅0

)∈ 𝛺t

}= inf

{𝜗 > 0|Φt

𝛶 |K(𝜗𝜐0

||𝜅0)> 0

}.

(9)𝜗a(𝜐0, 𝜅0

)= inf

{𝜗 > 0|Φ𝛶 |K

(𝜗𝜐0

||𝜅0)> 1 − 𝛼

},

(10)𝜗t,a(𝜐0, 𝜅0

)= inf

{𝜗 > 0|Φt

𝛶 |K(𝜗𝜐0

||𝜅0)> 1 − 𝛼

}.

2 For computational issues on smoothing techniques applied, see Bădin et al. (2010, 2019).3 In order to account for banks’ scale effects, we apply for the estimation of the unconditional and con-ditional frontiers’ data envelopment analysis (DEA) estimators under the variable returns to scale (VRS) (Banker et al. 1984).

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As has been explained in Bădin et al. (2012), by comparing the Q ratios as a func-tion of T we capture the marginal effects of time on banks’ shift of the frontier (i.e. banks’ technological change levels). When we compare Q� (for median α values, i.e.� = 0.5 ), we investigate the effect of time on the distribution of banks’ efficiency levels (i.e. technological catch-up). Given the adopted orientation, a decreasing non-parametric smooth regression line signifies an average positive effect of time (as if time acts as an extra free disposable input). In the opposite case, an increasing non-parametric smooth regression line signifies an average negative effect of time (as if time acts as an extra ‘bad’ output), whereas, a horizontal nonparametric smooth regression line indicates a neutral effect.

3.3 Convergence Methodology: The Phillips and Sul (2007) Approach

As a second step of our analysis, we investigate convergence within the panel of dynamic efficiency scores of 73 Indian banks, for which we utilize the con-vergence methodology of Phillips and Sul (2007). Given the scope of this study investigating the evolution of banks’ time dependent efficiency over a 19-year period, the use of the Phillips and Sul method is perfectly suitable. In addi-tion, this methodology is superior to the classical �-convergence approach (see Matousek et al. 2015 for a discussion). The applied approach accounts for transi-tional heterogeneity utilizing transition coefficients, hit , representing the bank’s efficiency score share yit , in relation to the average efficiency score. Using the relative transition coefficients, the ‘logt’ test can be presented as:

In Eq. (12) � measures the convergence’s magnitude and speed whereas Ht = N−1

∑N

i=1

�hit − 1

�2 . The convergence is indicated when � ≥ 2 , whereas � val-ues 2 > 𝛾 ≥ 0 imply conditional convergence. Using the log t test, we can determine the existence of convergence. In addition, Phillips and Sul (2007, 2009) propose a clustering mechanism tool which identifies the presence of club formation that may be present in the absence of whole panel convergence. The clustering test, as detailed in Phillips and Sul (2007), is a 4-step procedure which consists of repeated logt tests that start with the formation of a core club, and new members are subse-quently added or dropped to identify any convergent or divergent clubs.

(11)Q =�t(�0, �0

)

�(�0, �0

) , Q� =�t,a

(�0, �0

)

�a(�0, �0

)

(12)LogH1

Ht

− 2log(logt) = � + �logt + ut, for t = T0,… , T


1 3

4 Empirical Findings

4.1 Time Dependent Efficiency Scores

As has been explained in the methodology section, we evaluate banks’ efficiency levels under the assumption of VRS in order to include possible scale effects among the evaluated banks over the entire period. Table 2 presents the original efficiency estimates ranked by their VRS efficiency values. Looking at the top 15 best perform-ing banks, the results show that almost half of them are state-owned banks, namely IDBI Bank Ltd, Bank of India, State Bank of India, Bank of Baroda, Union Bank of India, Canara Bank, and Punjab National Bank. Six of the top performers are private banks; Kotak Mahindra Bank Ltd, ICICI Bank Ltd, City Union Bank Ltd, Karur Vysya Bank Ltd, Shamrao Vithal Co-op Bank Ltd and Federal Bank Ltd while there is only one cooperative bank (Bharat Co-op. Bank (Mumbai) Ltd, and one foreign bank (Bank of Nova Scotia) among the top. In contrast, we identify the majority of the worst performers as banks with foreign ownership namely; HSBC Ltd, DBS Bank Ltd, Societe Generale, Mashreq bank Plc, Barclays Bank Plc, Abu Dhabi Commercial Bank and HSBC Oman SAOG. Our findings support the earlier stud-ies (Sanyal and Shankar 2011; Tabak and Tecles 2010) indicating higher efficiency levels for state-owned banks in relation to foreign-owned banks.

Moreover, Fig. 1 presents the density plots of the obtained efficiency estimates across selected years.4 Specifically Fig. 1a presents the efficiency density plots for the years 1996, 2000 and 2004, whereas, Fig. 1b for 2007, 2010 and 2014. As can be identified, during 1996 we have the twin peak phenomenon indicating that the mass of banks’ efficiency levels was concentrated at 0.55 and 0.7 levels. However, during 2000 the mass was concentrated at 0.5 efficiency level and during 2004 at about 0.6 (Fig. 1a). When looking at Fig. 1b during the onset of Global Financial Crisis (GFC), we observe that the mass of banks’ efficiency levels was at 0.75 level. However, during the GFC (i.e. 2011), there is an indication of an overall increase in banks’ efficiency levels with the mass located at 0.8. Finally, for the year 2014 we observe a platykurtic distribution of banks’ efficiency levels suggesting large vari-ations within the efficiency estimates among the examined banks. This empirical finding suggests that we have a hetero-chronic effect of the GFC on the Indian bank-ing industry. Hence, the impact of the GFC on the Indian banks was felt well after the crisis period in sharp contrast to EU/US banks.

Figure 2 presents diachronically banks’ mean efficiency scores based on their ownership structure. The results reveal that up to 2004 and especially for the period 1996–2002, banks’ efficiency levels were fairly similar with private-owned and cooperative banks showing higher efficiency levels. However, after 2002 and especially for the period post-2005 the overall picture changes. State-owned banks which started off as the laggards in terms of efficiency at the outset quickly moved to dominate the Indian banking sector with the highest efficiency

4 The density plots of the VRS efficiency estimated for all the years are available upon request.

225

1 3


Tabl

e 2

Ran

king

s of t

he In

dian

ban

ks b

ased

on

thei

r mea

n es

timat

ed e

ffici

ency

scor

es o

ver 1

996–

2014

Ban

k na

me

Ow

ners

hip

Effici

ency

Ban

k na

me

Ow

ners

hip

Effici

ency

I D B

I BA

NK

LTD

.ST

A0.

9523

STA

TE B

AN

K O

F B

IKA

NER

& JA

IPU

RST

A0.

6474

KO

TAK

MA

HIN

DR

A B

AN

K L

TD.

PRI

0.78

99N

K G

S B

CO

-OP.

BA

NK

LTD

.CO

O0.

6448

BAN

K O

F IN

DIA

STA

0.77

90K

AR

NA

TAK

A B

AN

K L

TD.

PRI

0.64

14BA

NK

OF

NO

VA S

COTI

AFO

R0.

7702

CATH

OLI

C S

YR

IAN

BA

NK

LTD

.PR

I0.

6407

I C I

C I

BAN

K L

TD.

PRI

0.75

31TA

MIL

NA

D M

ERCA

NTI

LE B

AN

K L

TD.

PRI

0.63

79B

HA

RA

T CO

-OP.

BA

NK

(MU

MBA

I) L

TD.

COO

0.73

41C

ENTR

AL

BAN

K O

F IN

DIA

STA

0.63

69ST

ATE

BA

NK

OF

IND

IAST

A0.

7206

STA

TE B

AN

K O

F M

AU

RIT

IUS

LTD

.FO

R0.

6361

CIT

Y U

NIO

N B

AN

K L

TD.

PRI

0.72

06ST

ATE

BA

NK

OF

HY

DER

ABA

DST

A0.

6349

KA

RUR

VY

SYA

BA

NK

LTD

.PR

I0.

7198

COSM

OS

COO

BA

NK

LTD

.CO

O0.

6335

BAN

K O

F BA

ROD

AST

A0.

7173

VIJ

AYA

BA

NK

STA

0.61

79U

NIO

N B

AN

K O

F IN

DIA

STA

0.71

52PU

NJA

B &

SIN

D B

AN

KST

A0.

6170

CAN

AR

A B

AN

KST

A0.

7104

BAN

K O

F C

EYLO

NFO

R0.

6120

SHA

MR

AO

VIT

HA

L CO

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BA

NK

LTD

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I0.

7092

BAN

K O

F M

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AR

ASH

TRA

ST

A0.

6095

PUN

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NA

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NA

L BA

NK

STA

0.70

40K

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BA

NK

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6046

FED

ERA

L BA

NK

LTD

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I0.

7009

BAN

K O

F A

MER

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N A

FOR

0.60

29SY

ND

ICA

TE B

AN

KST

A0.

6991

JAM

MU

& K

ASH

MIR

BA

NK

LTD

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A0.

6017

SAR

ASW

AT

COO

BA

NK

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O0.

6991

STA

ND

AR

D C

HA

RTER

ED B

AN

K—

IND

IA B

RA

NC

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FOR

0.60

11IN

DU

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AN

K L

TD.

PRI

0.69

22H

D F

C B

AN

K L

TD.

PRI

0.60

10ST

ATE

BA

NK

OF

TRAV

AN

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A0.

6891

A B

BA

NK

LTD

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5843

LAK

SHM

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BAN

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TD.

PRI

0.68

72BA

NK

OF

BAH

RA

IN &

KU

WA

IT B

SCFO

R0.

5805

IND

IAN

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ERSE

AS

BAN

KST

A0.

6860

CIT

IBA

NK

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0.54

47SO

UTH

IND

IAN

BA

NK

LTD

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I0.

6852

AB

HY

UD

AYA

CO

-OP.

BA

NK

LTD

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O0.

5386

COR

POR

ATI

ON

BA

NK

STA

0.68

18U

NIT

ED B

AN

K O

F IN

DIA

STA

0.53

25ST

ATE

BA

NK

OF

MY

SOR

EST

A0.

6784

DEU

TSC

HE

BAN

K A

GFO

R0.

4990

OR

IEN

TAL

BAN

K O

F CO

MM

ERC

EST

A0.

6762

NEW

IND

IA C

O-O

P. B

AN

K L

TD.

COO

0.49

87BA

NK

OF

TOK

YO

-MIT

SUB

ISH

I U F

J LT

D.

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0.67

61C

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IT A

GR

ICO

LE C

OR

POR

ATE

& IN

VST

. BA

NK

FOR

0.49

59


1 3

STA

= S

tate

-ow

ned;

PR

I = P

rivat

e-ow

ned;

CO

O =

Coo

pera

tive;

FO

R =

For

eign

-ow

ned

Tabl

e 2

(con

tinue

d)

Ban

k na

me

Ow

ners

hip

Effici

ency

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k na

me

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ners

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227

1 3


estimates. Moreover, foreign-owned banks are reported to have the lowest effi-ciency levels, especially during the GFC period.

Finally, in Fig. 3, we present the findings from the second stage nonparamet-ric regression analysis comparing the Q ratios as a function of time. As it has been described by Jeong et al. (2010), we utilize a cross-validated local linear estimator (also see, Li and Racine 2004). The subfigures ‘a’, ‘c’, ‘e’, ‘g’ and ‘i’ present graphically the effect of time on banks’ technological shift, whereas, the

a

b

01

23

4

0 .2 .4 .6 .8 1x

VRS 1996 VRS 2000VRS 2004

01

23

4

0 .2 .4 .6 .8 1x

VRS 2008 VRS 2011VRS 2014

Fig. 1 Kernel density plots of the efficiency scores


1 3

subfigures ‘b’, ‘d’, ‘f’, ‘h’ and ‘k’ present the effect of time on bank’s technologi-cal catch-up.5

When we examine the entire sample subfigures (a and b) we observe a negative effect of time on change in banks’ technological frontier for the period 1996–2004. This negative effect is illustrated with an increasing nonparametric line. However, after 2004 and for the rest of the examined period the effect becomes positive as demonstrated by a decreasing nonparametric line. In a similar way, a negative effect of time on banks’ catch-up levels is observed for the period 1996–2002, whereas, the effect becomes positive after this point. When we consider foreign-owned banks, we observe a similar phenomenon but with different turning points. For technologi-cal change, the effect is negative just before the start of the GFC. However, the effect of time on foreign owned banks’ technological frontier becomes positive beyond this time point (Fig. 3c). However, for the case of technological catch-up (Fig. 3d) we observe that the negative effect on foreign banks’ technological catch-up levels is more pronounced up to 2011, which, after that time point slightly turns to be posi-tive signifying a recovery time point after GFC. For the private-owned banks, we observe a negative impact of time on their technological change up to early 2000’s (Fig. 3e). After that point and for the period 2001–2010, the effect turns positive and then becomes negative. Moreover, the effect of time on private-owned banks’ tech-nological catch-up levels is reported to be negative (Fig. 3f).

Fig. 2 Diachronical representations of banks’ efficiency scores based on their ownership structure

5 The estimated nonparametric line is presented in those plots alongside 95% estimated bootstrap inter-vals (Hayfield and Racine 2008).

229

1 3


Fig. 3 Time effects of techno-logical change and technological catch-up


1 3

When we consider the case of cooperative banks’, we can observe a nonlin-ear negative effect for the period 1996–2005 on their technological change levels (Fig. 3g). However, for the rest of the period the effect turns positive. In contrast when we examine the time effects on their technological catch-up, we observe a positive effect over the period 1996–2002, whereas, a negative effect over the period 2002–2014 is noted (Fig. 2h). Finally, for state-owned banks, it is evident that during 1996–2004 the effect of time on banks’ technological change is mixed as reflected in a highly nonlinear nonparametric regression line. However, for the rest of the period the effect is positive with a steeper negative line during the period 2004–2007. This finding suggests that the gains on state-owned banks’ technologi-cal change levels were greater before the GFC period in contrast with the period 2007–2014, in which, again the effects are positive, but, the gains are less as denoted by a less steep negative nonparametric regression line (Fig. 3i). Finally, Fig. 3k sig-nifies nonlinear effects of time on state-owned banks’ technological catch-up levels. It must be noted that for the period 1996–2002, the effect was negative. However, after that year the effect was positive.

4.2 Convergence Results

In order to analyse whether convergence in time-dependent efficiency levels is pre-sent among the four main types of banks that co-exist in India following the bank-ing reforms of 1991 and 1998, we implement Phillips and Sul (2007)’s panel con-vergence and club clustering methods. Our panel of 73 banks is categorised on the basis of the ownership structure i.e. (i) all banks, (ii) state-owned, (iii) private, (iv) cooperatives and (v) foreign banks to encapsulate the effects of the widespread banking reforms. The logt test results are tabulated in Table 3 and show that the null hypothesis of convergence is rejected for all 4 panels at the 5% significance level for both periods. These results point to two main conclusions. Firstly, the lack of group convergence over the period 1996–2014 suggests that heterogeneity in Indian bank-ing efficiency is prevalent and secondly, bank structure as a common basis does not seem to be a driving factor for convergence in efficiency. However, as in Phillips and Sul (2009), the presence of club convergence could be the reason why the null of overall convergence is rejected as the log t regression test has power against such cases.

Table 3 Phillips and Sul Logt convergence test on the input-oriented efficiency scores

*Indicates rejection of the null hypothesis of convergence at 5% sig-nificance level

γ t-stat

All banks − 1.026 − 11.061*State-owned banks − 0.657 − 2.991*Private owned banks − 1.366 − 4.440*Cooperatives − 1.758 − 3.127*Foreign-owned − 1.042 − 4.600*

231

1 3


Tabl

e 4

Clu

b co

nver

genc

e te

st re

sults

Dat

a se

tγ

t-sta

ts

All

73 In

dian

Ban

ks 1

996-

2014

Clu

b 1:

11

Stat

e-ow

ned

bank

s (A

llaha

bad

Ban

k, A

ndhr

a B

ank,

Can

ara

Ban

k, D

ena

Ban

k, In

dian

Ban

k, Ja

mm

u &

Kas

hmir

Ban

k Lt

d, N

aini

tal B

ank

Ltd.

, Pun

jab

& S

ind

Ban

k, P

unja

b N

atio

nal B

ank,

Sta

te B

ank

of B

ikan

er &

Jaip

ur, S

tate

Ban

k of

M

ysor

e), 7

priv

ate

bank

s (C

ity U

nion

Ban

k Lt

d, F

eder

al B

ank

Ltd.

, HD

FC B

ank

Ltd.

, IC

ICI B

ank

Ltd.

, Kot

ak M

ahin

dra

Ban

k Lt

d., T

amiln

ad M

erca

ntile

Ban

k Lt

d., A

xis b

ank

ltd.),

4 C

o-op

erat

ive

bank

s (A

bhyu

daya

Co-

op. B

ank

Ltd.

, Bha

rat

Co-

op. B

ank

(Mum

bai)

Ltd.

, Bom

bay

Mer

cant

ile C

o-op

. Ban

k ltd

., N

ew In

dia

Co-

op. B

ank

ltd.),

6 F

orei

gn b

anks

(Ban

k of

C

eylo

n, B

ank

of T

okyo

-Mits

ubis

hi U

FJ L

td.,

Bar

clay

s Ban

k pl

c, C

itiba

nk N

A, D

euts

che

Ban

k A

G, D

BS

Ban

k Lt

d.)

0.3

0.40

9

Clu

b 2:

3 S

tate

-ow

ned

bank

s (B

ank

of M

ahar

asht

ra, I

ndia

n O

vers

eas B

ank,

Orie

ntal

Ban

k of

Com

mer

ce),

4 Pr

ivat

e ba

nks

(Dha

nlax

mi B

ank

ltd.,

Indu

sind

Ban

k ltd

., K

arna

taka

ban

k ltd

., K

arur

Vys

ya B

ank

Ltd.

) 3 C

o-op

erat

ive

bank

s (C

itize

ncre

dit

Co-

op. b

ank

ltd.,

Cos

mos

co-

oper

ativ

e ba

nk lt

d., K

apol

co-

op. b

ank

ltd.),

2 F

orei

gn b

anks

(AB

ban

k ltd

., H

SBC

Ltd

.)

0.52

50.

681

Clu

b 3:

6 S

tate

-ow

ned

bank

s (B

ank

of In

dia,

Cen

tral b

ank

of In

dia,

Sta

te b

ank

of In

dia,

Syn

dica

te b

ank,

Uni

on b

ank

of In

dia,

U

nite

d ba

nk o

f Ind

ia),

1 co

-ope

rativ

e ba

nk (N

K G

S B

co-

op. B

ank

ltd.)

1 fo

reig

n ba

nks (

Abu

Dha

bi c

omm

erci

al b

ank)

− 1.

173

− 0.

975

Clu

b 4:

4 S

tate

-ow

ned

bank

s (B

ank

of B

arod

a, C

orpo

ratio

n ba

nk, S

tate

ban

k of

Hyd

erab

ad, U

CO b

ank)

, 2 p

rivat

e ba

nks (

Lak-

shm

i Vila

s ban

k ltd

., Sh

amra

o V

ithal

co-

op. b

ank

ltd.),

2 c

o-op

erat

ive

bank

s (G

reat

er B

omba

y co

-op.

ban

k ltd

., Sa

rasw

at

co-o

pera

tive

bank

ltd.

) 1 fo

reig

n ba

nk (B

ank

of B

ahra

in &

Kuw

ait B

SC)

0.61

30.

380

Clu

b 5:

3 S

tate

-ow

ned

bank

s (I D

B I

bank

ltd.

, Sta

te b

ank

of T

rava

ncor

e, V

ijaya

ban

k), 1

priv

ate

bank

(Sou

th In

dian

ban

k ltd

.)−

0.70

4−

0.47

6C

lub

6: 2

priv

ate

bank

s (C

atho

lic S

yria

n ba

nk lt

d, D

CB

b b

ank

ltd.),

2 fo

reig

n ba

nks (

Ban

k of

Am

eric

a N

A, B

ank

of N

ova

Scot

ia)

− 0.

153

− 0.

146

Stat

e-ow

ned

bank

sC

lub

1: A

llaha

bad

Ban

k, A

ndhr

a B

ank,

Can

ara

Ban

k, D

ena

Ban

k, In

dian

Ban

k, In

dian

Ove

rsea

s ban

k, Ja

mm

u &

Kas

hmir

Ban

k Lt

d, N

aini

tal B

ank

Ltd.

, Pun

jab

& S

ind

Ban

k, P

unja

b N

atio

nal B

ank,

Sta

te B

ank

of B

ikan

er &

Jaip

ur, S

tate

Ban

k of

M

ysor

e

0.43

90.

580

Clu

b 2:

Ban

k of

Mah

aras

htra

, Orie

ntal

Ban

k of

Com

mer

ce−

4.21

8−

0.90

9C

lub

3: B

ank

of B

arod

a, B

ank

of In

dia,

Cen

tral b

ank

of In

dia,

Cor

pora

tion

Ban

k, ID

BI b

ank

Ltd.

, Sta

te b

ank

of H

yder

abad

− 0.

630

− 1.

124

Priv

ate-

owne

d ba

nks

Clu

b 1:

City

Uni

on B

ank

Ltd,

Fed

eral

Ban

k Lt

d., H

DFC

Ban

k Lt

d., I

CIC

I Ban

k Lt

d, In

dusi

nd b

ank

ltd.

− 1.

890

− 1.

259

Clu

b 2:

Cat

holic

Syr

ian

bank

ltd,

DC

B b

ank

ltd.,

Dha

nlax

mi b

ank

ltd−

2.04

6−

1.12

0Fo

reig

n ba

nks


1 3

Tabl

e 4

(con

tinue

d)

Dat

a se

tγ

t-sta

ts

Clu

b 1:

Abu

Dha

bi c

omm

erci

al b

ank,

Ban

k of

Nov

a Sc

otia

, Mas

hreq

bank

PSC

, Soc

iete

Gen

eral

e, S

tand

ard

char

tere

d ba

nk -

Indi

a br

anch

es, S

tate

ban

k of

Mau

ritiu

s ltd

., IN

G V

ysya

ban

k ltd

., Ro

yal b

ank

of S

cotla

nd n

v.

0.87

71.

108

Clu

b 2:

A B

ban

k ltd

., B

ank

of A

mer

ica

NA

, Ban

k of

Bah

rain

& K

uwai

t BSC

, Ban

k of

Cey

lon,

Ban

k of

Tok

yo-M

itsub

ishi

U

FJ L

td.,

Bar

clay

s ban

k pl

c, C

itiba

nk N

A, D

euts

che

bank

A G

, HSB

C lt

d., C

redi

t Agr

icol

e co

rpor

ate

& in

vst.

bank

− 0.

723

− 0.

741

Clu

b 3:

D B

S b

ank

ltd.,

HSB

C b

ank

Om

an S

A O

G2.

639

1.91

1C

oope

rativ

e ba

nks

Clu

b 1:

Abh

yuda

ya C

o-op

. Ban

k Lt

d., B

hara

t Co-

op. B

ank

(Mum

bai)

Ltd.

, Bom

bay

Mer

cant

ile C

o-op

. Ban

k ltd

, Kap

ol c

o-op

. ba

nk lt

d., N

ew In

dia

co-o

p. b

ank

ltd.

− 1.

039

− 1.

159

Clu

b 2:

Citi

zenc

redi

t co-

op. b

ank

ltd.,

Cos

mos

co-

oper

ativ

e ba

nk lt

d., G

reat

er B

omba

y co

-op.

ban

k ltd

0.81

40.

407

*Ind

icat

es re

ject

ion

of th

e nu

ll hy

poth

esis

of c

onve

rgen

ce a

t the

5%

sign

ifica

nce

leve

l

233

1 3


The club convergence results confirm that this is indeed the case as we find the overwhelming presence of several clusters of convergence, as presented in Table 4 across all 4 types of banks. For the whole panel of 73 banks, we identify 6 clusters, with each cluster typically regrouping all 4 types of banks. The first club is the biggest comprising 11 state-owned banks, 7 private banks, 4 co-operative banks and 6 foreign banks. However, the speed of convergence for all 6 clusters is slow with γ ranging between 0.3 and − 1.173. We therefore note that the type of convergence relates to the rate of change in efficiency levels as γ < 2. The other 5 club formations have smaller number of banks and we note weak convergence rates in 2 of the clusters.

When we test for club convergence among each type of bank ownership, we find the presence of up to 3 convergent clusters. The composition of the clusters very closely mirrors those of the clubs obtained when the whole panel is tested so there is consistency in the formation of the clubs. This finding suggests that the type of bank ownership is, partly, a determining factor in efficiency of Indian banks. Other driving factors are likely to be other bank-specific factors such as geographical loca-tion. In addition, two other observations can be made from the club convergence results. Firstly, for the group of private banks, we identify very weak convergence (γ = − 1.890, − 2.046). This would suggest that heterogeneity in efficiency lev-els for private banks is present throughout the period 1996–2014. Secondly, the panel of foreign banks exhibit the highest speed of convergence amongst all panels (γ = 2.639). Again the ownership structure is likely to be a driving force. Overall, our club convergence results, to some extent, tally with those of Kumar and Gulati (2009) and Kumar (2013) who found the presence of �-convergence for the case of Indian public sector banks’ efficiency levels.

5 Conclusions

After having undergone a major transformation since the financial reforms were introduced in the 1990s, the banks in India now comprise of state-run, private, for-eign and cooperative banks. In this study, we analysed the time dependent efficiency scores for a panel of 73 banks over the period 1996–2014. Interestingly we identify several state-owned banks being the most efficient in the panel, while the majority of the worst performers are foreign banks. However, this was not always the case as during the period 1996–2002, as all types of banks’ efficiency levels were fairly sim-ilar with privately-owned and cooperative banks showing higher levels of efficiency. However, after 2002 and especially during the post-2005 period, state-owned banks which started off as the less efficient banks in the panel overtook all other types of banks to assert themselves as the top performers with regard to efficiency.

Our results also indicate an increase in bank efficiency during the global finan-cial crisis, as Indian banks continue to have limited exposure to global markets due to the country’s closed capital account. However, for the year 2014, we find large variations in the efficiency levels for the banks and we attribute the diversity in the estimates to the growth slowdown around that time. The negative impact of the cri-sis was quite significant for the foreign banks. Overall, we note that the banking sector in India is resilient and has weathered the financial crisis differently to their


1 3

counterparts in other emerging and developed economies. Looking at the impact of time on banks’ technological change and catch-up levels, we observe a nega-tive effect of time on banks’ technological change between 1996 and 2004, and on banks’ technological catch-up during 1996–2002. Since 2002 until the end of 2014, we observe a positive effect of time on banks’ technological change and catch-up. This no doubt coincides with massive investment in technology, especially by state-owned banks.

Convergence tests on the efficiency of banks reveal that although overall group con-vergence is absent for the period 1996–2014, club convergence is identified among all four types of banks throughout the sample period. This is evident from the large num-ber of convergent clusters made up of banks from all 4 types of ownership. We find that the composition of the clusters is partly driven by the ownership structure.

With the Indian economy expected to undergo further transformation with poli-cies directed at being more business-conducive and with better targeting of infla-tion reflecting macroeconomic stability, the Indian banking sector finds itself on the brink of further transformation, including the process of consolidation of state-owned banks. Demand for better services, the growing spread of mobile and inter-net banking, the need to invest in technology upgrading and innovation are some of the new and continuing challenges that the banks will face as the economy lurches ahead. Going forward, Indian banks are likely to face further pressure on asset qual-ity and capitalisation as the volume of NPLs in sectors facing difficulties is likely to increase, adding to credit risks. In addition, the implementation of Basel III poses further challenges with capital demand expected to rise. Banks with weak capitalisa-tion will find themselves in more vulnerable position. However, the path ahead for Indian banks will continue to further depend on government’s capital injections and financial support which will undoubtedly have a direct impact on bank performance.

Acknowledgements The authors would like to thank the editor of this journal for two rounds of rewriting on an earlier version of this paper. We are also thankful to Roman Matousek for his useful comments in the early stage of this work. We are solely responsible for any error that might yet remain.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.

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Affiliations

Sushanta Mallick1 · Aarti Rughoo2 · Nickolaos G. Tzeremes3 · Wei Xu4

Aarti Rughoo [email protected]

Nickolaos G. Tzeremes [email protected]

http://orcid.org/0000-0003-3851-3680

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Wei Xu [email protected]

1 School of Business and Management, Queen Mary University of London, Mile End Road, London E1 4NS, UK

2 Hertfordshire Business School, University of Hertfordshire, Hatfield AL10 9AB, UK3 Department of Economics, University of Thessaly, 28th October Street, 38333 Volos, Greece4 Department of Mathematics, Ryerson University, Toronto, Canada

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